Publication Details

VGEN: Fast Vertical Mining of Sequential Generator Patterns

FOURNIER-VIGER Philippe, GOMARIZ Antonio, ŠEBEK Michal and HLOSTA Martin. VGEN: Fast Vertical Mining of Sequential Generator Patterns. In: Data Warehousing and Knowledge Discovery. Munich: Springer Verlag, 2014, pp. 476-488. ISBN 978-3-319-10159-0. Available from: http://dx.doi.org/10.1007/978-3-319-10160-6_42
Czech title
VGEN: Rychlé vertikální dolování sekvenčních generátorů
Type
conference paper
Language
english
Authors
Fournier-Viger Philippe, M.Sc., Ph.D. (UMONC)
Gomariz Antonio (UMURC)
Šebek Michal, Ing. (DIFS FIT BUT)
Hlosta Martin, Ing. (DIFS FIT BUT)
URL
Keywords
sequential patterns, generators, vertical mining, candidate pruning
Abstract
Sequential pattern mining is a popular data mining task with wide applications. However, the set of all sequential patterns can be very large. To discover fewer but more representative patterns, several compact representations of sequential patterns have been studied. The set of sequential generatorsis one the most popular representations. It was shown to provide higher accuracy for classification than using all or only closed sequential patterns. Furthermore, mining generators is a key step in several other data mining tasks such as sequential rule generation. However, mining generators is computationally expensive. To address this issue, we propose a novel mining algorithm namedVGEN (Vertical sequential GENerator miner). An experimental study on five real datasets shows that VGEN is up to two orders of magnitude faster than the state-of-the-art algorithms for sequential generator mining.
Published
2014
Pages
476-488
Proceedings
Data Warehousing and Knowledge Discovery
Conference
16th International Conference on Data Warehousing and Knowledge Discovery, Mnichov, DE
ISBN
978-3-319-10159-0
Publisher
Springer Verlag
Place
Munich, DE
DOI
BibTeX
@INPROCEEDINGS{FITPUB10572,
   author = "Philippe Fournier-Viger and Antonio Gomariz and Michal \v{S}ebek and Martin Hlosta",
   title = "VGEN: Fast Vertical Mining of Sequential Generator Patterns",
   pages = "476--488",
   booktitle = "Data Warehousing and Knowledge Discovery",
   year = 2014,
   location = "Munich, DE",
   publisher = "Springer Verlag",
   ISBN = "978-3-319-10159-0",
   doi = "10.1007/978-3-319-10160-6\_42",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/10572"
}
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